Controlling physiological conditions by controlling environmental conditions

ABSTRACT

In one embodiment, a method comprising: (a) receiving a set of physiological data associated with at least one health condition of an animal subject; (b) receiving a set of environmental data associated with one or more environment conditions to which the subject is or has been exposed; (c) determining a set of operating parameters for at least one environmental device based at least partially on at least a portion of the set of physiological data and at least a portion of the set of environmental data; and (d) transmitting the set of operating parameters to the at least one environmental device to at least partially control at least one controlled environmental condition to which the subject is exposed to thereby at partially control the at least one health condition.

REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/135,287, filed Apr. 21, 2016, which claims the benefit of U.S.Provisional Application No. 62/150,669, filed, Apr. 21, 2015, and herebyincorporated by reference.

FIELD

This disclosure relates generally to controlling physiologicalconditions by controlling environmental conditions, and, moreparticularly, to techniques for controlling environmental conditionsaffecting circadian biorhythms using real-time biometrics.

BACKGROUND

Light entering the eye has been discovered not only to facilitatevision, but also to cause various non-visual biological effects. Forexample, certain studies have revealed that environmental light is theprimary stimulus for regulating circadian rhythms, seasonal cycles, andneuroendocrine responses. In some cases, and in particular with regardto biological responses to light, the environment can be controlled(e.g., to add more or less blue light) for various purposes. Forexample, light near the blue portion of the light spectrum can be usedas a therapeutic tool in the treatment of sleep disorders and SeasonalAffective Disorders (SAD), such as “winter depression.” In such cases,the exposure to short wavelength light in the range of 440 to 480 nm canbe controlled to suppress the melatonin secretion by the pineal glandand affect the circadian rhythm.

Conventional techniques for environmental controls, however, generallyfail to consider the state and changes of state of a subject and/or thesubject's environment. For example, a factory with a night shift mightadd more blue light to stimulate physiological response (i.e., to keepworkers alert and productive). However, such environmental controls failto consider the real-time response of the subjects to the changingenvironmental conditions.

Therefore, approaches are needed to address the problem of observing aparticular subject's physiological conditions, and modifying theenvironmental conditions in response to the observed aspects. Thepresent invention fulfills this need, among others.

SUMMARY

The following presents a simplified summary of the invention in order toprovide a basic understanding of some aspects of the invention. Thissummary is not an extensive overview of the invention. It is notintended to identify key/critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome concepts of the invention in a simplified form as a prelude to themore detailed description that is presented later.

Some embodiments of the present disclosure address observing aparticular subject's environmental and physiological conditions, andmodifying the environmental conditions in response to the observations.Other embodiments are directed to approaches for making biometricobservations and analyzing them so as to recommend or make changes toenvironmental conditions using biorhythm feedback signals. A particularembodiment is disclosed for controlling environmental conditionsaffecting circadian biorhythms using real-time biometrics.

Accordingly, one aspect of the invention is a method, which, in oneembodiment comprises: (a) receiving a set of physiological dataassociated with at least one health condition of an animal subject; (b)receiving a set of environmental data associated with one or moreenvironment conditions to which the subject is or has been exposed; (c)determining a set of operating parameters for at least one environmentaldevice based at least partially on at least a portion of the set ofphysiological data and at least a portion of the set of environmentaldata; and (d) transmitting the set of operating parameters to the atleast one environmental device to at least partially control at leastone controlled environmental condition to which the subject is exposedto thereby at partially control the at least one health condition.

Another aspect of the invention is system, which, in one embodimentcomprises: a processor; and memory operatively connected to theprocessor and configured to instruct the processor to execute thefollowing steps: (a) receiving a set of physiological data associatedwith at least one health condition of an animal subject; (b) receiving aset of environmental data associated with one or more environmentconditions to which the subject is or has been exposed; (c) determininga set of operating parameters for at least one environmental devicebased at least partially on at least a portion of the set ofphysiological data and at least a portion of the set of environmentaldata; and (d) transmitting the set of operating parameters to the atleast one environmental device to at least partially control at leastone controlled environmental condition to which the subject is exposedto thereby at partially control the at least one health condition.

Yet another aspect of the invention is a computer-readable medium,which, in one embodiment comprises a medium configured with thefollowing instructions for execution by a processor: (a) receiving a setof physiological data associated with at least one health condition ofan animal subject; (b) receiving a set of environmental data associatedwith one or more environment conditions to which the subject is or hasbeen exposed; (c) determining a set of operating parameters for at leastone environmental device based at least partially on at least a portionof the set of physiological data and at least a portion of the set ofenvironmental data; and (d) transmitting the set of operating parametersto the at least one environmental device to at least partially controlat least one controlled environmental condition to which the subject isexposed to thereby at partially control the at least one healthcondition.

Further details of aspects, objectives, and advantages of the disclosureare described below and in the detailed description, drawings, andclaims. Both the foregoing general description of the background and thefollowing detailed description are exemplary and explanatory, and arenot intended to be limiting as to the scope of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described below are for illustration purposes only. Thedrawings are not intended to limit the scope of the present disclosure.

FIG. 1A depicts an environment in which embodiments of the presentdisclosure can operate.

FIG. 1B depicts an environment in which embodiments of the presentdisclosure can operate.

FIG. 1C depicts a feedback path between bio-factors and environmentalconditions, according to some embodiments.

FIG. 1D1, FIG. 1D2, and FIG. 1D3, depict techniques for reducing formsof therapeutic bias.

FIG. 2 shows a system for controlling environmental conditions affectingcircadian biorhythms using real-time biometrics, according to someembodiments.

FIG. 3A depicts a learning model development flow as used in systems forcontrolling environmental conditions affecting circadian biorhythmsusing real-time biometrics, according to some embodiments.

FIG. 3B depicts an environmental parameter synthesis flow as used insystems for controlling environmental conditions affecting circadianbiorhythms using real-time biometrics, according to some embodiments.

FIG. 4 is a visual representation of a light modulation technique asused in systems for controlling environmental conditions affectingcircadian biorhythms using real-time biometrics, according to someembodiments.

FIG. 5 is a block diagram of a system for controlling environmentalconditions affecting circadian biorhythms using real-time biometrics,according to an embodiment.

FIG. 6 is a simplified diagram illustrating an optical device, accordingto an embodiment of the present disclosure.

FIG. 7 depicts exemplary architectures of components suitable forimplementing embodiments of the present disclosure and/or for use in theherein-described environments.

DETAILED DESCRIPTION

The present invention relates generally to observing a particularsubject's past and/or current environmental and physiologicalconditions, and modifying the environmental conditions to which thesubject is exposed to control a health condition of the subject inresponse to the observed environmental and physiological conditions. Inone embodiment, the system is configured to receive a set ofphysiological data associated with at least one health condition of ananimal subject and a set of environmental data associated with one ormore environment conditions to which the subject is, has been or will beexposed. Next, the system determines a set of operating parameters forat least one environmental device based at least partially on at least aportion of the set of physiological data and at least a portion of theset of environmental data. The system then transmits the set ofoperating parameters to the at least one environmental device to atleast partially control at least one controlled environmental conditionto which the subject is exposed to thereby at partially control the atleast one health condition. These elements are discussed in detail belowand in connection with different embodiments.

Although this application describes the invention in terms of thesubject being a human, it should be considered that the invention may beapplied to other animals, such as, for example, farm animals (e.g., cowsand chickens) to increase productivity, or ranch animals (e.g., cattle)to decrease anxiety in processing facilities.

As used herein, a health condition refers to a property of an animalbody capable of measurement, including, for example, body temperature,pulse, electrical activity of the heart (as measured, for example, byelectrocardiogram (ECG)), electrical activity of the brain (as measured,for example, by an electroencephalogram (EEG)), activity of motorneurons (as measured, for example, by electromyography (EMG)), bloodpressure, pupil dilatation, skin conductance, skin color, bloodoxygenation, saliva and blood constituent levels—e.g., sugar, insulin,cholesterol, hormones (e.g. melatonin, cortisol, thyroid stimulatinghormone, prolactin, serotonin, adrenalin, dopamine, gastrin, growthhormone, insulin, estrogen, progesterone, testosterone, etc.), whiteblood cell count, iron, etc., or any combination of one or more of theaforementioned conditions. In some embodiments, of particular interestare measurable conditions that are indicative (either directly orindirectly) of the circadian cycle, herein referred to as measurablecircadian cycle indicative conditions (MCCICs). Many of the conditionsabove are MCCICs, including, for example, body temperature, pulse, pupildilation, and relative levels of certain hormones, such as melatonin,and the interrelations of these conditions. As used herein, MCCIC alsoincludes circadian cycle indicative conditions discovered or developedin the future.

As used herein, a set of physiological data includes one or moremeasurements of at least one health condition of an animal body. The setof physiological data can range from just one datum to many data. Themeasurement of these health conditions is well known and will not beconsidered in detail here. Suitable methods to acquire biometrics datacomprise direct readings through a sensor in contact with the subject(body temperature/heart beat sensor), remote detection techniques(thermal imaging, remote radar such as Doppler radar and others,movement sensors), measurement of assays (blood/saliva . . . ), directcontact bio-chemical measurements (through a measuring device connectedto the body, such as a chip pinned to a finger the collects hormone orglucose level) etc. The biometric data may be acquired by dedicatedsensors, or by sensors which are attached to another device (forinstance, a peripheral to a computer system/smart device, or a ‘smart’accessory attached to a domestic appliance such as a light source).Smart snap accessories are known in the art and disclosed for example inU.S. Pat. No. 9,488,324, hereby incorporated by reference.

Generally, although not necessarily, it is preferable for the healthcondition measurement to be simple and noninvasive, and to avoid theneed for assays or other consumable materials for performing themeasurement. For example, although melatonin may be measured using thesubject's saliva or blood as mentioned above, it is generally preferredthat the relative circadian state be determined indirectly through morereadily determined, conditions such as, for example, pulse temperatureand pupil dilation. Obviously, as new measurement techniques aredeveloped for internal properties, such as body chemistry (e.g.,blood/saliva constituents), there may be a preference for measuringhormones and the like directly.

As specific examples of biometric measurements, the color of the skin ofthe subject (in a specific part of the body) may be measured by anoptical sensor. A simple example of optical sensor is a camera (such asa charge-coupled device-CCD) such as the camera embarked on smartphonesand other devices. In some cases, the optical measurement is performedunder a known light source, so that the measurement can be interpretedwith high accuracy (indeed, it is commonly easier to infer the color ofan object when using a known light source rather than having to inferboth the color of the object and the color of the light source). Forinstance, an embarked light-emitting diode (LED) source may be used forillumination when the optical measurement is performed. A simple exampleof such light source is the flash unit found on smartphones and otherdevices; another example is the screen of smart devices—which can forinstance be set to emit white light with a known spectrum.

Besides a standard RGB (red, green, and blue spectrum) camera (such asthat of a smartphone), other optical measurement devices can beemployed. For instance, rather than three color sensors (red, green,blue), more sensors may be used in order to get more information on thereflectance spectrum of the measured object (such as the skin of thesubject). We refer to such sensors (with more than three spectralchannels) as “spectrometers”. In some embodiments a spectrometer isimplemented by combining a standard CCD sensor (such as that of acamera) with a diffraction grating. Then CCD can be used for measuring aspectrum. This conversion from camera to spectrometer may be done on-thefly—either automatically or manually. In some embodiments, aspectrometer system can be calibrated at first by a direct measurementof a known light source, or a direct measurement of a known objectilluminated by a known light source. For instance, the light emitted bya flash LED (or by a display screen) may be shined on a white wall or amirror and measured with the spectrometer to calibrate thespectrometer's response. After this, the spectrometer may be used forspectral and reflectance measurements.

In another example, an additional biosensor is used to measure theamount of melatonin in the subject (for instance in his saliva) in orderto gain further information on his circadian cycle. Other hormones andchemicals can likewise be measured. The measurement can be performed bycollecting a biological sample and measuring in a separate setup, or bya bio-reading system attached to the subject.

In another example, pupil dilation is measured. This can, for instance,be measured by a dedicated system, or by a simple imaging technique on asmart device (i.e. imaging of the user's eye by a smartphone cameraunder illumination by the phone's flash light).

In addition, health conditions may include medical assessments of asubject's health by a doctor, or a medical test.

As used herein, environmental conditions are measurable conditions ofthe environment to which the subject animal was exposed, is exposed, orwill be exposed. For example, environmental conditions may includetemperature, time, time zone, location of the animal, presence of theanimal, duration of the presence of the animal, humidity, wind, windchill, precipitation, barometric pressure, sun rise and sun set, dewpoint, tides, smog index/air quality, Ultraviolet (UV) index,sound/noise, and light exposure, diet, drug intake, or any combinationof one or more of the aforementioned conditions. Light exposure is abroad term that relates to the type and intensity of the light receivedby the animal. The type of light is typically, although not necessarily,related to the spectral power distribution (SPD), which, specifically,is a measurement describing the power per unit area per unit wavelengthof an illumination (radiant exitance), and, more generally, refers tothe concentration, as a function of wavelength, of any radiometric orphotometric quantity may relate to spectrum. Of particular interestherein, is exposure of the animal to the blue light spectrum, which isconnected with influencing the circadian cycle as described above. Otherlight spectrums that may be monitored, include, for example, ultravioletlight and infrared light. In addition to the SPD emitted by a lightsource or the diffuse SPD in a given environment, other measures ofinterest may include optical quantities (SPD, radiance, irradiance) at aspecific point or in a specific direction, for instance entering thesubject's pupil or impinging on the subject's skin.

The set of environment data corresponds to one or more measures of atleast one environmental condition mentioned above. The set ofenvironmental data can range from just one datum to many data. Themeasurement of the environmental conditions above using environmentsensors is well known and will not be considered herein in detail.

Some embodiments of the invention address the need for quantifying astimulus received by a human subject. For instance, there may be a needto monitor the amount of light received by a subject over a period oftime (which may span hours, days, weeks, months or other periods oftimes). Further, there may be a need to measure other quantities relatedto light exposure. This includes the spectrum of the light, its CCT, itscolor rendering, etc. Embodiments of the invention comprise a devicewith an optical sensor, which is worn by the subject; as the subject isexposed to light, the sensor records data pertaining to exposure. Thisdata can be accumulated over time to estimate the light exposure of thesubject. This data can be used in a computer system to influence thecircadian stimulation of the subject.

These measurements of a property of light may be coupled with othermeasurements, including biometric measurements. For instance, abiometric device may measure the body temperature of a subject, oxygenlevels in their blood, bodily levels of hormones, etc. Thesemeasurements may be correlated with the light measurements, for instanceto draw an inference on an aspect of the patient's health.

In various embodiments, the light-sensing devices are placed at a knownposition relative to the subject's body so that their light reading canbe interpreted quantitatively. In some embodiments, the light sensor isoften or always exposed to light—for instance, it may be desirable toplace the sensor on a watch or other wearable, rather than on a phone,which may be placed in a pocket. In other embodiments, the sensor isplaced on a computer on which the user is working—for instance an officecomputer.

In a specific example, the subject wears a smart watch, which comprisesseveral optical sensors—including a spectrometer. The sensors measurethe intensity and spectrum of the ambient light. Since the watch has aknown position on the body, the measurements can be interpreted tomeasure how much light the subject receives over time, and how much ofthis light is in a spectral range stimulating the circadian system (forinstance, illuminance in the range 430-490 nm). Periodically, a sensoris also used to measure the reflectance spectrum of the skin of theuser's arm—this is performed by directing the spectrometer towards thearm, emitting light from the watch's screen and collecting the reflectedlight from the arm with the spectrometer sensor. Further, the watchmeasures the wrist skin's temperature. The information on ambient light,temperature and skin reflectance is then combined by a computer systemto infer an aspect of the patient's health or circadian cycle. Based onthis data, an amount of circadian stimulation is determined over thecourse of a day. Specifically, this amount is used by a computer systemto determine a necessary amount of supplementary light required by thesubject. Accordingly, at a later time, the subject receives a specificdose of light with a specific circadian stimulation level from a lightsource, which is part of the embodiment.

In some embodiments of the invention, such measurements are used tocalibrate information on the subject. For instance, biometrics may becollected over a period of time to obtain a baseline about the subject.This baseline may be related to various aspects of health, includingaspects of the circadian rhythm (this includes sleep patterns, levels ofhormones related to the circadian cycle etc. . . . ). Likewise, lightexposure may be collected over a period of time to characterize thesubject's habits or typical environment. Once a baseline is established,deviations from the baseline can be measured (either to monitor drift inthe subject's condition, or to assess his response to a treatment).Subject-specific calibration may be desirable because baseline biometricsignals may vary significantly from individual to individual; therefore,it may not be possible to interpret biometric readings in the absence ofan initial calibration phase. Any of the aforementioned calibrationtechniques can be used singly or in combination, and can be usedtogether with techniques to assess and remove therapeutic bias frommeasured observations (e.g., see FIG. 1D).

A controllable environmental condition refers to the above-mentionedenvironmental conditions that can be controlled, at least in part, by anenvironmental device.

An environmental device is a device that at least in part controls acontrollable environmental condition. Examples of environmental devices,include, for example, lighting devices, HVAC components (e.g.thermostats, air conditioners, furnaces, heaters, air purifiers, fansand other air handling devices), humidifiers, dehumidifiers, soundsystems (e.g., speakers and noise cancelling apparatus), and shade orshutter controls, or any combination of one or more of theaforementioned conditions. Of particular interest herein are lightingdevices, which can be used for a variety of lighting applications,including, for example, ambient lighting, spot light and backlighting ofdisplay screens. In one embodiment, the lighting devices are configuredto control the type and/or intensity of the light emitted. Such lightingdevices are known and are disclosed for example in U.S. patentapplication Ser. No. 14/531,545, hereby incorporated by reference.

It should be noted that the measurements of environmental conditions andhealth conditions can be contemporaneous, historical, or anticipatory(i.e. predictive). Additionally, the measurements may be based on ashort sampling period, e.g., immediate/a few seconds, or they may bebased on a longer sampling period—e.g. hours, days, weeks, months oryears. For example, in one embodiment, the exposure to light may bemeasured over hours, while temperature may be measured instantaneously.For example, in some embodiments, the health conditions are used todetermine the instant health state of the subject (for instance whetherthe heart rate or another biometric is within a specified range), or thehistory of the health state of the subject (amount of exercise over aperiod, level of a hormone such as melatonin over time, evolution ofbody temperature, etc.). In the latter case the historical healthcondition may span a time of a few minutes (sudden change inbiometrics), a few hours (light exposure throughout the day, dietthroughout the day) or several days (sleep cycle and travel over daysand weeks). One of skill in the art will understand the appropriate termfor recording the measurement for the given application in light of thisdisclosure. Future environmental conditions may for instance be obtainedas an input from the user (plans to be in a specific location, travelplan, diet plants). Future health conditions may be inferred from amodel (predetermined model, machine-learning model . . . ) which maytake current/past health and environmental conditions as data.

Determining the operating parameters typically comprises using a simplealgorithm. For example, the algorithm may compare the set ofphysiological data and the set of environmental data to one or morepredetermined limits and then using logic operators based on thecomparison to establish the operating parameters for the at least oneenvironmental device. For example, if the melatonin level is below acertain limit and the exposure to blue is above a certain limit, thenset the operating parameters to reduce the dose of blue light to acertain limit to induce the production of melatonin. In this example,the logical operators of “if/then”, “greater than,” “less than,” and“and” were used to determine the operating parameters. Those of skill inthe art will readily understand how to determine operating parametersusing logical operators in light of this disclosure.

Machine learning may be used in a variety of embodiments to establish orrefine the algorithm. Machine learning may be based on large data sets(e.g. data relating environmental conditions and health conditions, asknow from healthcare studies), on the inputs described above (health andenvironmental conditions), and on other specific knowledge about thesubject. Examples of machine learning techniques include neuralnetworks, Bayesian inference and other methods known in the art.

Generally, although not necessarily, the system and method of thepresent invention, determines a target controllable environmentalcondition and then determines the operating parameters to achieve thetarget. The environmental device is configured to receive the operatingparameters, and adjust or otherwise generate an output according to theoperating parameters to reach the controllable environmental condition.Although a target controllable environmental condition may be used, itis not necessary. For example, in some embodiments, the operatingparameters are determined by trial and error to achieve a target healthcondition of the subject. This may be done without knowing or measuringthe actual controllable environmental condition being controlled. Inother embodiments, the controllable environmental condition isexplicitly measured and a feedback loop may be used to adapt theparameters of the environmental device.

The herein disclosed embodiments address the aforementioned problem byreceiving environmental data from an environment (e.g., location,temperature, barometric pressure, lighting conditions, spectral contentof ambient light, etc.) and physiological data from a subject (e.g.,pulse rate, blood-oxygen level, blood-sugar level, melatonin level,etc.), processing the data, and using a learning model to determine anoptimal set of environmental parameters and settings to broadcast to aset of environmental devices that can affect the environmentalconditions. The techniques can continually receive new data, andsynthesize and broadcast new environmental settings in real time toenable systems for controlling environmental conditions affectingcircadian biorhythms using real-time biometrics.

For example, in one embodiment, from inferences on the health state of asubject, an algorithm may prescribe a desired change to environmentalconditions. For instance, if the circadian cycle of the subject isout-of-phase, a light therapy can be provided (increase of blue lightand of light intensity to entrain the circadian cycle, or paucity ofblue light and reduced intensity to reduce circadian stimulation). Inanother example, the temperature and humidity of the ambient air aretuned to affect the subject's body temperature. A closed-feedback loopmay be used between health conditions and environmental conditions.

Various embodiments include controllable light sources so that thesubject is exposed to a given amount of light with a given spectrum at agiven time in order to influence his circadian cycle. For instance, ifit has been determined that the subject has not received enoughcircadian-stimulating blue light in the first few hours of the day, hemay be exposed to a large amount of blue light from a computer screenmid-morning to compensate for this. Conversely, if it has beendetermined that the user has received excessive amounts ofcircadian-stimulating blue light throughout the day, lights can beautomatically dimmed or tuned to a spectrum containing less blue light(including a low-CCT spectrum, or a standard CCT spectrum with verylittle blue light and violet light instead to achieve a desiredchromaticity) at night.

Reference is now made to certain embodiments shown in the figures. Thedisclosed embodiments are not intended to be limiting of the claims.

FIG. 1A and FIG. 1B depict operations within environment 100. As shownin FIG. 1A, environment 100 comprises various computing systems (e.g.,servers and devices) interconnected by a network 108. The network 108can comprise any combination of a wide area network (e.g., WAN), localarea network (e.g., LAN), wireless network, cellular network, wirelessLAN (e.g., WLAN), or any such means for enabling communication ofcomputing systems. The network 108 can also be referred to as theInternet. More specifically, environment 100 comprises at least oneinstance of a control server 110, an instance of a biometric device 106₁ (e.g., smart watch, wristband monitor, etc.) worn by a subject 105 ₁,an instance of an environmental sensor 108 ₁ (e.g., motion detector,temperature sensor, etc.), and an instance of an environmental device112 ₁ (e.g., light modulating device, overhead troffer, ambientluminaires, task lighting, standalone orb, etc.). In one or moreembodiments, information detected by the biometric device 106 ₁ and theenvironmental sensor 108 ₁ can be received by the control server 110 andused to control the environmental device 112 ₁. In some cases, thecontrol server 110 can control other light modulating devices, such as asmart phone 113, a tablet 114, a laptop 116, and the display of aworkstation 117.

As shown, the control server 110, the biometric device 106 ₁ formeasuring a health condition, the environmental sensor 108 ₁ formeasuring an environment condition, and the environmental device 112 ₁for controlling a controllable environmental condition, can exhibit aset of high-level interactions (e.g., operations, messages, etc.). Inthis embodiment, the shown high-level interactions can representinteractions in systems for controlling environmental conditionsaffecting circadian biorhythms using real-time biometrics. Morespecifically, a learning model for determining desired environmentalconditions based, in part, on a subject's environmental andphysiological state can be developed (e.g., trained, simulated,optimized, etc.) at the control server 110 (see operation 122 of FIG.1B). The environmental sensor 108 ₁ can send environmental data (seemessage 124) and the biometric device 106 ₁ can send physiological datafrom the subject 105 ₁ (see message 126) to the control server 110 forprocessing. For example, the environmental data represents environmentalobservations detected by the environmental sensor 108 ₁ (e.g., location,temperature, barometric pressure, lighting conditions, spectral contentof ambient light, etc.), and the physiological data represents biometricobservations detected by the biometric device 106 ₁ (e.g., pulse rate,blood-oxygen level, blood-sugar level, melatonin level, etc.). Thecontrol server 110 can process (e.g., filter, adjust, translate, etc.)the received data (see operation 128) and validate and/or makeadjustments to the learning model as needed (see operation 130 of FIG.1B). Using the updated learning model and the current environmental andphysiological data, an optimal set of environmental parameters andsettings can be determined (see operation 132) and broadcast to theenvironmental device 112 ₁ and other devices (see message 134). Asshown, the control server 110 can continually receive new data, andsynthesize and broadcast new environmental settings in real time (seecontinuous operations 140). One embodiment of a system for implementingthe techniques shown in FIG. 1A and disclosed herein is shown in FIG. 2.

FIG. 1C depicts a feedback path 1C00 between bio-factors andenvironmental conditions. The shown subject conditions include passivestates (e.g., see “Are”) as well as active states (see “Doing”). Thesubject conditions are used to effect environmental conditions orchanges to environmental conditions so as to effect a therapy, or inother control a health condition of the subject. The therapy can includeblue light therapies (e.g., reduction or increase in blue light) thatare facilitated by violet-emitting LEDs.

FIG. 1D1, FIG. 1D2, and FIG. 1D3, depict techniques for reducing formsof therapeutic bias. The aforementioned therapies can cause changes inthe subject, and such changes can be included in measurements taken (forexample) using biometric devices. When incorporating feedback (e.g.,measured responses in the subject, determining patterns, etc.), the newmeasurements can include the effect of the therapy. Such effects cansometimes be included in the new measurements as a therapeutic bias.Such biases can be calculated and removed, for example, prior todelivering new observations to the learning model. In some cases apattern can be identified and/or a change in patterns can be identified,and the therapy can be prescribed so as to counteract undesired effectsof a pattern or to counteract undesired effects of changes in a pattern.This is illustrated in FIGS. 1D1-1D3. In a first phase, a pattern P1 ofan input observation (which could be biometric and/or environmentaldata) is collected (D1), and a constant (for instance) amount of atherapeutic dose is administered (D2); the impact on a biometric factorof interest are measured (D3) and is broken down into two components:one stemming from the variation of input observation, and another fromthe therapeutic dose. Once this breakdown is established, in subsequentphase Pattern P2, therapeutic doses 1D2 are adapted to the inputobservations 1D1 so that the biometric factor 1D3 is maintained at adesired value.

FIG. 2 shows one embodiment of a system 200 for controllingenvironmental conditions affecting circadian biorhythms using real-timebiometrics. The system 200 shown in FIG. 2 presents an exampleembodiment of various modules for implementing the herein disclosedtechniques, and operated by the control server 110 from FIG. 1A. Thesystem 200 further comprises a subject environment 220 that includes aset of one or more environmental sensors (e.g., environmental sensor1081 ₁ to environmental sensor 108 _(N)), and a set of one or morebiometric devices (e.g., biometric device 106 ₁ to biometric device 106_(N)) worn by a respective set of subjects (e.g., subject 105 ₁ tosubject 105 _(N)). The subjects in the subject environment 220 areexposed to conditions determined, in part, by a set of environmentaldevices (e.g., environmental device 112 ₁ to environmental device 112_(N)). As shown in system 200, a discriminator 212 can receive data(e.g., over the network 108) from the environmental sensors andbiometric devices, and can process (e.g., filter, eliminate, adjust,translate, etc.) the received data to generate a statistically reliableset of current data 231 to be stored in the observed data 213. In somecases, periods of observations can be eliminated when the data isdetermined to potentially bias forward control calculations. Asimulation engine 214 can use the current data 231 and other data (e.g.,target outcomes 233) to develop and adjust a learning model 232 storedin a store of model data 216. The output of the simulation engine 214 isreceived by a parameter synthesizer 218 to convert to parameters andsettings specific to the instances of the environmental devices thathave been identified for adjustment. As an example, the discriminator212 might receive data that indicates a subject has just arrived at acertain work area and needs additional lighting. The data might furtherindicate the subject has a high melatonin level (e.g., is sleepy). Inthis example, the simulation engine 214 can simulate the desiredresponse to these inputs using the learning model to determine moreambient light with a higher blue light intensity is needed in thecertain work area. The parameter synthesizer 218 can identify theenvironmental devices (e.g., IP address, device ID, etc.) and determinethe device-specific settings (e.g., commands, etc.) to communicate tophysically effect the desired response in real time. More detailsrelated to the operations of the components of the system 200 aredescribed as pertains to FIG. 3A and FIG. 3B.

FIG. 3A depicts a learning model 312 development flow 3A00 as used insystems for controlling environmental conditions affecting circadianbiorhythms using real-time biometrics. The learning model developmentflow 3A00 shown in FIG. 3A comprises a set of operations that can beexecuted by the discriminator 212 and the simulation engine 214described in FIG. 2 . Additional or fewer steps, and/or other allocationof operations are possible. Specifically, the learning model developmentflow 3A00 can be used to develop, validate, and update a learning modelused in systems for controlling environmental conditions affectingcircadian biorhythms using real-time biometrics. More specifically, thelearning model development flow 3A00 can start a determination ofwhether such a system is being initially setup (see decision 302). Ifthis is an initial setup, the simulation engine 214 can read a set oftraining data from the model data 216 (see step 304). For example, thetraining data can include target outcomes 233, experimental data,expected conditions and biometrics, subject data, etc. Using thetraining data, a learning model (e.g., the learning model 232) can begenerated (see step 306). For example, the learning model can comprise aset of mappings (e.g., linear, non-linear, logical, algorithmic, etc.)of input data (e.g., detected environmental and physiological data) tooutput data (e.g., environmental settings and parameters). If this isnot an initial setup, the discriminator will receive a current set ofdata from the environmental sensors and biometric devices (see step308), and filter and convert the received data for use by the simulationengine 214 (see step 310). For example, the discriminator 212 mightreceive an HTTP message from a smart sensor (e.g., “DID=1234”) thatrequires parsing and extracting of the pertinent information (e.g.,“temp=26.52”), and conversion to a syntax acceptable by the simulator(e.g., “did.1234.temp=26.52”).

FIG. 3B depicts an environmental parameter synthesis flow 3B00 as usedin systems for controlling environmental conditions affecting circadianbiorhythms using real-time biometrics. The environmental parametersynthesis flow 3B00 shown in FIG. 3B comprises a set of operations thatcan be executed by the simulation engine 214 and the parametersynthesizer 218 described in FIG. 2 . Additional or fewer steps, and/orother allocation of operations are possible. Specifically, theenvironmental parameter synthesis flow 3B00 can be used to generate thedevice-specific parameters and settings 328 that will yield a set ofenvironmental adjustments to optimally affect circadian biorhythms usingreal-time biometrics. More specifically, the environmental parametersynthesis flow 3B00 can continue from the process shown in FIG. 3A todetermine a best fit model response to the current data 231 receivedfrom the environment and subject (see step 322). For example, thesimulation engine 214 can use various techniques (e.g., sensitivityanalyses, parameter sweeps, Monte Carlo analyses, etc.) to determine theresponse from the learning model 232 that best fits the target outcomes233 given the current environmental and physiological conditionsassociated with the current data 231. The best fit model output isdelivered to the parameter synthesizer 218 to convert to device-specificsettings (see step 324). For example, the simulation engine 214 outputmight call for an increase in blue light in a designated work area, andthe parameter synthesizer might then convert that output to a specificdevice (e.g., led lamp “led.1122”) and a device-specific setting (e.g.,“led.1122.b=7.85”). In some cases, the parameter synthesizer 218 canalso adjust for current device settings (see step 326). For example, agiven environmental device to be adjusted may be in a current stateand/or at a current setting (e.g., at a range limit, a relative setting,etc.) that might affect the desired adjustment. When all deviceparameters and settings have been determined and adjusted, the parametersynthesizer can broadcast the updated control information in a formatacceptable for each adjusted device. One embodiment and example of animplementation of the herein disclosed techniques is described aspertains to FIG. 4 .

FIG. 4 is a visual representation of one embodiment of a lightmodulation technique 400 as used in systems for controllingenvironmental conditions affecting circadian biorhythms using real-timebiometrics. The light modulation technique 400 is implemented in asubject environment 410 that includes a work area A 411 in proximity ofan environmental sensor 108 ₂ (e.g., motion detector) and anenvironmental device 112 ₂ (e.g., LED lamp). The representation furthercomprises a work area B 412 in proximity of an environmental sensor 108₃ (e.g., motion detector) and an environmental device 112 ₃ (e.g.,thermostat). A subject wearing a biometric device 106 ₂ (e.g.,“bio.id=106 ₂”) starts in a first position by work area A 411, and thenmoves to a second position by work area B 412. The light modulationtechnique 400 further depicts two examples of learning model logic: alearning model logic 421 and a learning model logic 422. Other types andimplementations of learning model logic can be deployed in otherexamples and embodiments.

When in the first position, learning model logic 421 is processed (e.g.,by the simulation engine 214). The learning model logic 421 shows thatif the biometric device 106 ₂ is within 10 feet of work area A 411(e.g., as detected by environmental sensor 108 ₂) and the subject'smelatonin level is greater than 500 (index value or arbitrary units forrelative comparison only) (e.g., as detected by the biometric device 106₂), then the blue spectrum of environmental device 112 ₂ should beincreased by 3.45 μW/cm2. When the subject is in the second position,learning model logic 422 is processed (e.g., by the simulation engine214). The learning model logic 422 shows that if the biometric device106 ₂ is within 10 feet of work area B 412 (e.g., as detected byenvironmental sensor 108 ₃) and the subject's heart rate level isgreater than 80 beats per minute (e.g., as detected by the biometricdevice 106 ₃), then the temperature near work area B 412 should bedecreased by 2.00° F. As shown, the herein disclosed approachimplemented in the light modulation technique 400 illustrates thecontrol of environmental conditions affecting circadian biorhythms usingreal-time biometrics.

In other examples, the learning model is based not only on aninstantaneous biometric reading, but also on other data including:current environmental data (temperature in the room, light spectrum andlevel in the room . . . ), past biometric data (level of a hormone,heart rate, body temperature, physical exertion through the day), pastenvironmental data (light level and spectrum, ambient temperaturethrough the day). For example, in one embodiment, the dose of blue lightreceived by the subject is measured throughout the day. In the evening,when the subject arrives home, the level of melatonin and bodytemperature are measured. Based on this environmental and biometricdata, an algorithm determines the current level of circadianentrainment. Based on this determination, and on past historical data onthe subject's living habits, a schedule of indoor light exposure isdetermined in order to control the circadian entrainment and bring thesubject to a state of sleepiness at the desired time. For instance, ifmelatonin suppression is observed, the dose of blue light in the indoorenvironment is reduced to reduce circadian entrainment (‘ifmelatonin<[determined level], reduce blue dose below [target level]).Further, the biometrics of the subject may be monitored through thenight. The environmental conditions (such as temperature) may be adaptedthrough the night in reaction with the subject's biometrics, forinstance to improve sleep quality (room temperature may be tuned inresponse to the subject's body temperature). The sleep cycle may bemonitored, and at a time that is adequate for wake-up (end of a sleepcycle that is close to a target wake-up time), lighting in the room isturned on and the blue dose is elevated to wake up the subject andsynchronize his circadian phase.

In another example, the subject travels across time zones. The travelschedule is known. Based on readings of light exposure and biometrics, alight therapy is devised to shift the circadian clock of the subjectover several days. In one embodiment, this done in anticipation oftravel across time zones. For example, when traveling to an earlier timezone, in one embodiment, the therapy starts several days before thetravel, for instance, a desired dose of blue light may be provided inthe early morning to shift the circadian cycle to an earlier phase, inpreparation for travel. Likewise, after returning from an earlier timezone, decreasing doses of blue light may be provided in the earlymorning to shift gradually the circadian cycle to an earlier phase,shifting the circadian clock by one or several hours each day.

Although the previous examples act on a contemporaneous timescale—influencing biological aspects over a few hours—the presentinvention applies to other therapeutic actions over a longer time scale.For instance, an environmental factor (light or other) may be controlledover a period of several days, weeks or months to have a desired healthaspect.

FIG. 5 is a block diagram of a system for controlling environmentalconditions affecting circadian biorhythms using real-time biometrics,according to some embodiments. The system 500 comprises at least oneprocessor and at least one memory, the memory serving to store programinstructions corresponding to the operations of the system. As shown, anoperation can be implemented in whole or in part using programinstructions accessible by a module. The modules are connected to acommunication path 505, and any operation can communicate with otheroperations over communication path 505. The modules of the system can,individually or in combination, perform method operations within system500. Any operations performed within system 500 may be performed in anyorder unless as may be specified in the claims. The shown embodimentimplements a portion of a computer system, presented as system 500,comprising a computer processor to execute a set of program codeinstructions (see module 510) and modules for accessing memory to holdprogram code instructions to perform: using a computing system having atleast one processor to perform a process, the process comprising;receiving a set of physiological data associated with a human subject(see module 520); receiving a set of environmental data associated witha set of environmental conditions, wherein the human subject is exposedto the environmental conditions, and wherein the environmentalconditions are determined at least in part by a set of environmentaldevices (see module 530); determining a set of parameters associatedwith a portion of the environmental devices, wherein the parameters arebased at least in part on a portion of the physiological data (seemodule 540), and broadcasting at least some of the parameters to effecta change in at least some of the environmental devices (see module 550).

In some embodiments, circadian rhythm control is sought to improve thesleep patterns of a subject. This is not always the case, however,Studies have shown that some cancerous cells are especially sensitive tothe circadian clock, and that a properly-timed circadian clockdisruption could selectively kill them (e.g. with higher sensitivitythan healthy cells). In some embodiments, circadian clock control isemployed as a form of cancer therapy.

FIG. 6 is a simplified diagram illustrating an optical device 600,according to an embodiment of the present disclosure. As shown in FIG. 6, an optical device 600 includes a submount 111 (not shown) that has asurface. A number of radiation sources are provided on the submount.According to various embodiments, two types of radiation sources areprovided, and each type of radiation source is associated with a rangeof wavelength. For example, radiation sources include a first pluralityof radiation sources that are configured to emit radiation characterizedby a first wavelength. More specifically, the first wavelength can havea range of between about 380 nm to 470 nm. In a specific embodiment, thefirst wavelength is characterized by a peak emission at about 420 nm to470 nm. The first plurality of radiation sources are positioned on thesurface. The first plurality of radiation sources have an n number ofradiation sources. For example, the first plurality of radiation sourcesincludes LED device 604 and LED device 605.

The radiation sources of optical device 600 also include a secondplurality of radiation sources that are configured to emit radiationcharacterized by a second wavelength. In various embodiments, the secondwavelength is shorter than the first wavelength. More specifically, thesecond wavelength is violet or ultraviolet. In a specific embodiment,the second plurality of radiation sources are characterized by a peakemission in the range of about 380 nm to about 430 nm. In certainembodiments, the second wavelength is less than 390 nm. The secondplurality of radiation sources is positioned on the surface of thesubmount. The second plurality of radiation sources comprises m numberof radiation sources. The ratio between m and n is predetermined basedon a selected wavelength. Typically, n is greater than m. The ratio of nto m can be 1:1, 2:1, 10:1, and other ratios. For example, the ratio isbased on a selected wavelength output for the optical device 500. As anexample, the second plurality of radiation sources comprises shortviolet LED device 603 and short violet LED device 606.

In various embodiments, the arrangement of the radiation sources ispatterned. More specifically, the locations of the second plurality ofradiation sources are predetermined and are covered and/or surrounded bya specific phosphor pattern (e.g., phosphor pattern 607 ₁, phosphorpattern 607 ₂). The phosphor pattern is configured to be proximal toinstances from among the second plurality of radiation sources. Morespecifically, the phosphor pattern is more remote from the firstplurality of radiation sources. The phosphor pattern is configured toabsorb at least a portion of radiation emitted by the second pluralityof radiation sources. In various embodiments, the phosphor pattern isassociated with a wavelength emission ranging from about 440 nm to about490 nm. In a specific embodiment, the phosphor pattern comprises bluephosphor material. For example, the patterned blue phosphor material isused to convert violet or ultraviolet radiation to blue light. Amongother things, the blue light converted by the patterned phosphormaterial can help create desired color balance and improve efficiencyeven while the intensity of the blue light is varied in accordance withblue light therapies.

Further details regarding general approaches to varying blue light forwhite light and for therapeutic purposes are described in U.S. Pat. No.8,740,413 B1, which is hereby incorporated by reference in its entirety.

As shown, the optical device 600 also includes a first wavelengthconverting layer 601 configured to absorb at least a portion ofradiation emitted by the first plurality of radiation sources and thesecond plurality of radiation sources. The first wavelength-convertinglayer is associated with a wavelength emission ranging from 590 nm to650 nm. For example, the first wavelength-converting layer comprises redphosphor material that is adapted to emit substantially red color light.

The second wavelength converting layers 601 and 602 are configured toabsorb at least a portion of radiation emitted by the first plurality ofradiation sources and the second plurality of radiation sources. Thesecond wavelength-converting layer is associated with a wavelengthemission ranging from 490 mn to 590 nm. For example, the secondwavelength-converting layer comprises a green phosphor that is adaptedto emit substantially green light.

As an example, the first and second wavelength-converting layer canabsorb radiation from both the first plurality and second plurality ofradiation sources. Additionally, the first and second wavelengthconverting layers may also absorb emission from the phosphor pattern. Itis to be appreciated that the embodiments of the present disclosure canprovide efficiency gains over conventional techniques.

In one embodiment, a first plurality of radiation sources configured toemit radiation characterized by a first wavelength ranging from about430 nm to about 480 nm, wherein the first plurality of radiation sourcesare positioned on the mounting surface and having n number of radiationsources that are situated in proximity to a second plurality ofradiation sources configured to emit radiation characterized by a secondwavelength shorter than the first wavelength. The second plurality ofradiation sources are positioned on the mounting surface and having mnumber of radiation sources. The ratio between n and m can bepredetermined based on a selected wavelength, or the effective ratio oflight emitted from the first plurality of radiation sources with respectto the second plurality of radiation sources can be varied undertherapeutic control.

For producing pleasing light, a wavelength-converting layer ispositioned in an optical path of radiation of at least one of the firstradiation sources, and the wavelength-converting layer is configured toabsorb radiation at the first wavelength. In exemplary embodiments, thefirst wavelength-converting layer has an emission wavelength in therange from about 480 nm to about 600 nm.

Further variations are possible in the design of the light emitter. Insome cases, the radiation sources are placed on a plurality of mountingmembers, rather than on a single submount or mounting member. Forinstance, a first set of LEDs emitting in a wavelength range 430-490 nmare placed on a first mounting member; and a second set of LEDs emittingin a wavelength range 400-430 nm are placed on a second mounting member.

In various embodiments, the invention comprises more than one radiationsource and the radiation sources can be driven independently. Forinstance an embodiment may comprise a first set of sources controlled bya first driver and a second set of sources controlled by a seconddriver. These drivers may be modulated by a computer or other automatedsystem according to embodiments of the invention, for instance in orderto vary a circadian stimulation.

Further, the light source can be characterized by a number of measuresquantifying the emitted light or the emitted spectral power distribution(SPD). This includes luminance, illuminance, radiation diagram,correlated color temperature (CCT), chromaticity, color rendering index(CRI) and other color rendition measures, fraction of the SPD in a givenspectral range (such as 400-430 nm or 430-490 nm). These measures can betuned dynamically, or can be kept at a given value, in accordance withembodiments of the invention. For instance, the CRI may be maintainedabove a desired value such as 80; the CCT may be varied within a desiredrange, such as from 3000K to 5000K; the fraction of the SPD in the range430-490 nm may be varied from less than 0.1% to more than 20%. All thesevariations may be performed by tuning the emitted spectrum—for instancein embodiments of the invention having independent strings of LEDs, byvarying the power in each string; in various embodiments this tuning isperformed by a computer or other automated system.

FIG. 7 depicts a block diagram of an instance of a computer system 700suitable for implementing embodiments of the present disclosure.Computer system 700 includes a bus 706 or other communication mechanismfor communicating information. The bus interconnects subsystems anddevices such as a CPU, or a multi-core CPU (e.g., data processor 707), asystem memory (e.g., main memory 708, or an area of random access memoryRAM), a non-volatile storage device or non-volatile storage area (e.g.,ROM 709), an internal or external storage device 710 (e.g., magnetic oroptical), a data interface 733, a communications interface 714 (e.g.,PHY, MAC, Ethernet interface, modem, etc.). The aforementionedcomponents are shown within processing element partition 701, howeverother partitions are possible. The shown computer system 700 furthercomprises a display 711 (e.g., CRT or LCD), various input devices 712(e.g., keyboard, cursor control), and an external data repository 731.

According to an embodiment of the disclosure, computer system 700performs specific operations by processor 707 executing one or moresequences of one or more program code instructions contained in amemory. Such instructions (e.g., program instructions 7021, programinstructions 7022, program instructions 7023, etc.) can be contained inor can be read into a storage location or memory from any computerreadable/usable medium such as a static storage device or a disk drive.The sequences can be organized to be accessed by one or more processingentities configured to execute a single process or configured to executemultiple concurrent processes to perform work. A processing entity canbe hardware-based (e.g., involving one or more cores) or software-based,and/or can be formed using a combination of hardware and software thatimplements logic, and/or can carry out computations and/or processingsteps using one or more processes and/or one or more tasks and/or one ormore threads or any combination therefrom.

According to an embodiment of the disclosure, computer system 700performs specific networking operations using one or more instances ofcommunications interface 714. Instances of the communications interface714 may comprise one or more networking ports that are configurable(e.g., pertaining to speed, protocol, physical layer characteristics,media access characteristics, etc.) and any particular instance of thecommunications interface 714 or port thereto can be configureddifferently from any other particular instance. Portions of acommunication protocol can be carried out in whole or in part by anyinstance of the communications interface 714, and data (e.g., packets,data structures, bit fields, etc.) can be positioned in storagelocations within communications interface 714, or within system memory,and such data can be accessed (e.g., using random access addressing, orusing direct memory access DMA, etc.) by devices such as processor 707.

The communications link 715 can be configured to transmit (e.g., send,receive, signal, etc.) communications packets 738 comprising anyorganization of data items. The data items can comprise a payload dataarea 737, a destination address 736 (e.g., a destination IP address), asource address 735 (e.g., a source IP address), and can include variousencodings or formatting of bit fields to populate the shown packetcharacteristics 734. In some cases the packet characteristics include aversion identifier, a packet or payload length, a traffic class, a flowlabel, etc. In some cases the payload data area 737 comprises a datastructure that is encoded and/or formatted to fit into byte or wordboundaries of the packet.

In some embodiments, hard-wired circuitry may be used in place of or incombination with software instructions to implement aspects of thedisclosure. Thus, embodiments of the disclosure are not limited to anyspecific combination of hardware circuitry and/or software. Inembodiments, the term “logic” shall mean any combination of software orhardware that is used to implement all or part of the disclosure.

The term “computer readable medium” or “computer usable medium” as usedherein refers to any medium that participates in providing instructionsto processor 707 for execution. Such a medium may take many formsincluding, but not limited to, non-volatile media and volatile media.Non-volatile media includes, for example, optical or magnetic disks suchas disk drives or tape drives. Volatile media includes dynamic memorysuch as a random access memory.

Common forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, or any other magneticmedium; CD-ROM or any other optical medium; punch cards, paper tape, orany other physical medium with patterns of holes; RAM, PROM, EPROM,FLASH-EPROM, or any other memory chip or cartridge, or any othernon-transitory computer readable medium. Such data can be stored, forexample, in any form of external data repository 731, which in turn canbe formatted into any one or more storage areas, and which can compriseparameterized storage 739 accessible by a key (e.g., filename, tablename, block address, offset address, etc.).

Execution of the sequences of instructions to practice certainembodiments of the disclosure is performed by a single instance of thecomputer system 700. According to certain embodiments of the disclosure,two or more instances of computer system 700 coupled by a communicationslink 715 (e.g., LAN, PTSN, or wireless network) may perform the sequenceof instructions required to practice embodiments of the disclosure usingtwo or more instances of components of computer system 700.

The computer system 700 may transmit and receive messages such as dataand/or instructions organized into a data structure (e.g.,communications packets 738). The data structure can include programinstructions (e.g., application code 703), communicated throughcommunications link 715 and communications interface 714. Receivedprogram code may be executed by processor 707 as it is received and/orstored in the shown storage device or in or upon any other non-volatilestorage for later execution. Computer system 700 may communicate througha data interface 733 to a database 732 on an external data repository731. Data items in a database can be accessed using a primary key (e.g.,a relational database primary key).

The processing element partition 701 is merely one sample partition.Other partitions can include multiple data processors, and/or multiplecommunications interfaces, and/or multiple storage devices, etc. withina partition. For example, a partition can bound a multi-core processor(e.g., possibly including embedded or co-located memory), or a partitioncan bound a computing cluster having plurality of computing elements,any of which computing elements are connected directly or indirectly toa communications link. A first partition can be configured tocommunicate to a second partition. A particular first partition andparticular second partition can be congruent (e.g., in a processingelement array) or can be different (e.g., comprising disjoint sets ofcomponents).

A module as used herein can be implemented using any mix of any portionsof the system memory and any extent of hard-wired circuitry includinghard-wired circuitry embodied as a processor 707. Some embodimentsinclude one or more special-purpose hardware components (e.g., powercontrol, logic, sensors, transducers, etc.). A module may include one ormore state machines and/or combinational logic used to implement orfacilitate the performance characteristics of the system.

Various implementations of the database 732 comprise storage mediaorganized to hold a series of records or files such that individualrecords or files are accessed using a name or key (e.g., a primary keyor a combination of keys and/or query clauses). Such files or recordscan be brought into and/or stored in volatile or non-volatile memory.

In the foregoing specification, the disclosure has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the disclosure. Forexample, the above-described process flows are described with referenceto a particular ordering of process actions. However, the ordering ofmany of the described process actions may be changed without affectingthe scope or operation of the disclosure. The specification and drawingsare, accordingly, to be regarded in an illustrative sense rather than ina restrictive sense.

What is claimed is:
 1. A system comprising: a processor; and memoryoperatively connected to the processor and configured to instruct theprocessor to execute the following steps: receive physiological dataassociated with at least one health condition of an animal subject;receive environmental data associated with one or more environmentconditions to which the animal subject is or has been exposed, whereinsaid environmental data comprises at least historical data over acertain period, wherein said certain period is at least one hour;determine operating parameters for at least one environmental devicehaving variable output based at least partially on at least a portion ofphysiological data and at least a portion of said historical data,wherein determining the operating parameters comprises at leastcomparing said at least one physiological data to a baseline measurementand either increasing or decreasing said output depending on whethersaid at least one physiological data is above or below said baseline,wherein the degree to which the output is increased or decreased dependsat least in part on said historical data; transmit the set of operatingparameters to the at least one environmental device to at leastpartially control the output to thereby at partially control the atleast one health condition; and wherein said environmental conditioncomprises at least one of temperature, time, time zone, location of theanimal, presence of the animal, duration of the presence of the animal,humidity, wind, wind chill, precipitation, barometric pressure, sun riseand sun set, dew point, tides, smog index/air quality, UV index,sound/noise, light exposure, diet, drug intake.
 2. The system of claim1, wherein said environmental device controls said environmentalcondition.
 3. The system of claim 1, wherein said environmentalcondition comprises at least smog index/air quality.
 4. The system ofclaim 1, wherein said environmental condition comprises at least UVindex.
 5. The system of claim 1, wherein said environmental conditioncomprises at least sound/noise.
 6. The system of claim 1, wherein saidenvironmental condition comprises at least diet.
 7. The system of claim1, wherein said environmental condition comprises at least drug intake.8. The system of claim 1, wherein determining the operating parameterscomprises comparing the at least a portion of physiological data and theat least a portion of environmental data to one or more predeterminedlimits and then using logic operators based on the comparison toestablish the operating parameters for the at least one environmentaldevice.
 9. The system of claim 8, wherein the predetermined limits areestablished using a learning program.
 10. The system of claim 8, whereinthe predetermined limits are established by altering the at least oneenvironmental condition and recording the change in the at least onehealth condition.
 11. The system of claim 1, wherein said animal is ahuman.
 12. The system of claim 1, wherein said set of environmental datais obtained at least partially from a wearable.
 13. A system comprising:a processor; and memory operatively connected to the processor andconfigured to instruct the processor to execute the following steps:receive a set of physiological data associated with at least one healthcondition of an animal subject; receive a set of environmental dataassociated with one or more environment conditions to which the animalsubject is or has been exposed, wherein said set of environmental datacomprises at least historical data corresponding to accumulated lightexposure of said subject over a certain period, wherein said certainperiod is at least one hour; determine a set of operating parameters forat least one environmental device having variable output based at leastpartially on at least a portion of the set of physiological data and atleast a portion of the set of environmental data, and whereindetermining the operating parameters comprises determining a change inoutput of said at least one environmental device based on said at leastone physiological data and said accumulated light exposure, whereindetermining the operating parameters comprises at least comparing saidat least one physiological data to a baseline measurement and eitherincreasing or decreasing said output depending on whether said at leastone physiological data is above or below said baseline, wherein thedegree to which the output is increased or decreased depends at least inpart on said accumulated light exposure; transmit the set of operatingparameters to the at least one environmental device to at leastpartially control the output to thereby at partially control the atleast one health condition; and wherein said environmental conditioncomprises at least one of temperature, time, time zone, location of theanimal, presence of the animal, duration of the presence of the animal,humidity, wind, wind chill, precipitation, barometric pressure, sun riseand sun set, dew point, tides, smog index/air quality, UV index,sound/noise, light exposure, diet, drug intake.
 14. The system of claim13, wherein said animal is a human.
 15. The system of claim 14, whereinsaid human is a shift worker.
 16. The system of claim 14, wherein saidset of environmental data is obtained at least partially from awearable.
 17. The system of claim 16, wherein said wearable is a smartwatch.
 18. The system of claim 16, wherein said wearable is a smartwatch.